Call me maybe: Methods and practical implementation of artificial intelligence in call center arrivals’ forecasting

Beyond the beaten paths of forecasting call center arrivals: on the use of dynamic harmonic regression with predictor variables

“…This suggests that the general type of forecasting approach and its possibility of including contextual factors in the form of predictor variables particularly affects prediction accuracy in a practical call arrival forecast setting. Overall, preliminary call center forecasting literature recognized the predictive potential of ML approaches (Albrecht et al 2021;Barrow 2016;Jalal et al 2016;Rausch and Albrecht 2020) but is still in its infancy and thus, we substantiate the knowledge on the performance of ML models.…”

“…RF was found to yield higher prediction accuracy for nearly all of the considered lead time constellations. Similar results were gathered in an extensive ML comparison study by Albrecht et al (2021). Besides, artificial neural networks such as multilayer perceptrons (Barrow 2016) and recurrent neural networks (Jalal et al 2016) attracted increasing attention.…”

“…Meanwhile, new methods apart from time series models were developed and investigated to predict call center arrivals with high accuracy (Albrecht et al 2021;Rausch and Albrecht 2020). Regression models (Aldor-Noiman et al 2009;Brown et al 2005;Ibrahim et al 2016;Ibrahim and L'Ecuyer 2013) and machine learning (ML) approaches like random forest (RF) and neural networks (Barrow 2016;Jalal et al 2016) were found to yield accurate predictions by including meaningful predictor variables.…”

Artificial intelligence focus and firm performance

“…Moreover, AI generates and assesses the promise of sales leads (Davenport et al, 2020), augmenting and elevating firm sales capabilities, to positively impact future sales (Grewal et al, 2017;Huang & Rust, 2018;Syam & Sharma, 2018). Albrecht et al (2021) establish that machine learning methods yield the most accurate forecasts, while Ma and Sun (2020) find that AI generates accurate predictions that assist marketing decisions and in turn improve all aspects of business performance. Thus, we expect that AI focus will improve marketing inputs and outputs.…”

“…Forecasts suggest AI will deliver an economic output of approximately $13 trillion by 2030 and create between $1.4 and $2.6 trillion of value in marketing (Chui et al, 2018). This will facilitate the processing of large scale and unstructured data in real-time, and by generating predictive insights that enhance marketing decision making (Albrecht et al, 2021).…”